Overview

Dataset statistics

Number of variables20
Number of observations5000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory781.4 KiB
Average record size in memory160.0 B

Variable types

Categorical3
Boolean5
Numeric12

Alerts

Salary is highly correlated with Base_pay and 6 other fieldsHigh correlation
Base_pay is highly correlated with Salary and 6 other fieldsHigh correlation
Bonus is highly correlated with Salary and 6 other fieldsHigh correlation
Unit_Price is highly correlated with Salary and 6 other fieldsHigh correlation
low is highly correlated with Salary and 6 other fieldsHigh correlation
Unit_Sales is highly correlated with Salary and 6 other fieldsHigh correlation
Total_Sales is highly correlated with Salary and 6 other fieldsHigh correlation
Months is highly correlated with Salary and 6 other fieldsHigh correlation
Salary is highly correlated with Base_pay and 6 other fieldsHigh correlation
Base_pay is highly correlated with Salary and 6 other fieldsHigh correlation
Bonus is highly correlated with Salary and 6 other fieldsHigh correlation
Unit_Price is highly correlated with Salary and 5 other fieldsHigh correlation
low is highly correlated with Salary and 6 other fieldsHigh correlation
Unit_Sales is highly correlated with Salary and 5 other fieldsHigh correlation
Total_Sales is highly correlated with Salary and 6 other fieldsHigh correlation
Months is highly correlated with Salary and 4 other fieldsHigh correlation
Salary is highly correlated with Base_pay and 6 other fieldsHigh correlation
Base_pay is highly correlated with Salary and 6 other fieldsHigh correlation
Bonus is highly correlated with Salary and 6 other fieldsHigh correlation
Unit_Price is highly correlated with Salary and 5 other fieldsHigh correlation
low is highly correlated with Salary and 6 other fieldsHigh correlation
Unit_Sales is highly correlated with Salary and 6 other fieldsHigh correlation
Total_Sales is highly correlated with Salary and 6 other fieldsHigh correlation
Months is highly correlated with Salary and 5 other fieldsHigh correlation
Age is highly correlated with Salary and 5 other fieldsHigh correlation
Salary is highly correlated with Age and 9 other fieldsHigh correlation
Base_pay is highly correlated with Age and 9 other fieldsHigh correlation
Bonus is highly correlated with Age and 9 other fieldsHigh correlation
Unit_Price is highly correlated with Salary and 7 other fieldsHigh correlation
openingbalance is highly correlated with closingbalance and 2 other fieldsHigh correlation
closingbalance is highly correlated with Salary and 8 other fieldsHigh correlation
low is highly correlated with Salary and 7 other fieldsHigh correlation
Unit_Sales is highly correlated with Age and 9 other fieldsHigh correlation
Total_Sales is highly correlated with Age and 8 other fieldsHigh correlation
Months is highly correlated with Age and 9 other fieldsHigh correlation
Education is highly correlated with Salary and 2 other fieldsHigh correlation
Salary has unique values Unique
Bonus has unique values Unique

Reproduction

Analysis started2021-12-05 14:29:57.794011
Analysis finished2021-12-05 14:31:58.634540
Duration2 minutes and 0.84 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
Male
2528 
Female
2472 

Length

Max length6
Median length4
Mean length4.9888
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowFemale
3rd rowMale
4th rowFemale
5th rowMale

Common Values

ValueCountFrequency (%)
Male2528
50.6%
Female2472
49.4%

Length

2021-12-05T20:02:00.051837image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-05T20:02:00.145573image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
male2528
50.6%
female2472
49.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Business
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
False
4200 
True
800 
ValueCountFrequency (%)
False4200
84.0%
True800
 
16.0%
2021-12-05T20:02:00.208080image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
False
3524 
True
1476 
ValueCountFrequency (%)
False3524
70.5%
True1476
29.5%
2021-12-05T20:02:00.254946image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Calls
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
True
4539 
False
461 
ValueCountFrequency (%)
True4539
90.8%
False461
 
9.2%
2021-12-05T20:02:00.317430image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Type
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
Month-to-month
2777 
Two year
1195 
One year
1028 

Length

Max length14
Median length14
Mean length11.3324
Min length8

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMonth-to-month
2nd rowMonth-to-month
3rd rowMonth-to-month
4th rowMonth-to-month
5th rowMonth-to-month

Common Values

ValueCountFrequency (%)
Month-to-month2777
55.5%
Two year1195
23.9%
One year1028
 
20.6%

Length

2021-12-05T20:02:00.411160image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-05T20:02:00.489266image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
month-to-month2777
38.4%
year2223
30.8%
two1195
16.5%
one1028
 
14.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Billing
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
True
2956 
False
2044 
ValueCountFrequency (%)
True2956
59.1%
False2044
40.9%
2021-12-05T20:02:00.559820image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Rating
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
False
3682 
True
1318 
ValueCountFrequency (%)
False3682
73.6%
True1318
 
26.4%
2021-12-05T20:02:00.622309image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Age
Real number (ℝ≥0)

HIGH CORRELATION

Distinct65
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.865
Minimum18
Maximum88
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2021-12-05T20:02:00.731654image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile38
Q147
median52
Q357
95-th percentile65
Maximum88
Range70
Interquartile range (IQR)10

Descriptive statistics

Standard deviation8.560691099
Coefficient of variation (CV)0.1650571888
Kurtosis0.8620040667
Mean51.865
Median Absolute Deviation (MAD)5
Skewness-0.2599714625
Sum259325
Variance73.28543209
MonotonicityNot monotonic
2021-12-05T20:02:00.934700image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50256
 
5.1%
53254
 
5.1%
55248
 
5.0%
54245
 
4.9%
51244
 
4.9%
56238
 
4.8%
52234
 
4.7%
49222
 
4.4%
47204
 
4.1%
57200
 
4.0%
Other values (55)2655
53.1%
ValueCountFrequency (%)
182
 
< 0.1%
193
 
0.1%
204
0.1%
218
0.2%
226
0.1%
237
0.1%
245
0.1%
254
0.1%
263
 
0.1%
273
 
0.1%
ValueCountFrequency (%)
882
 
< 0.1%
851
 
< 0.1%
821
 
< 0.1%
801
 
< 0.1%
791
 
< 0.1%
782
 
< 0.1%
765
0.1%
756
0.1%
749
0.2%
7310
0.2%

Salary
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct5000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99821.92855
Minimum5089
Maximum199970.74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2021-12-05T20:02:01.137810image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum5089
5-th percentile57978.14651
Q183890.33898
median100579.3785
Q3116912.0925
95-th percentile139003.3586
Maximum199970.74
Range194881.74
Interquartile range (IQR)33021.75349

Descriptive statistics

Standard deviation25376.96174
Coefficient of variation (CV)0.2542223148
Kurtosis0.9593172698
Mean99821.92855
Median Absolute Deviation (MAD)16440.20885
Skewness-0.3960416
Sum499109642.8
Variance643990187.4
MonotonicityStrictly increasing
2021-12-05T20:02:01.356507image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
136944.73691
 
< 0.1%
80831.252961
 
< 0.1%
87070.841051
 
< 0.1%
100185.43351
 
< 0.1%
92234.24131
 
< 0.1%
115437.00581
 
< 0.1%
112634.78531
 
< 0.1%
75590.507141
 
< 0.1%
134402.67761
 
< 0.1%
104797.23821
 
< 0.1%
Other values (4990)4990
99.8%
ValueCountFrequency (%)
50891
< 0.1%
5698.121
< 0.1%
5896.651
< 0.1%
6125.121
< 0.1%
62451
< 0.1%
6444.231
< 0.1%
6455.51
< 0.1%
6458.357221
< 0.1%
6529.231
< 0.1%
6682.331
< 0.1%
ValueCountFrequency (%)
199970.741
< 0.1%
195970.71
< 0.1%
192636.81
< 0.1%
185685.91
< 0.1%
180696.81
< 0.1%
175689.31
< 0.1%
170639.55651
< 0.1%
170372.54731
< 0.1%
169149.7071
< 0.1%
168974.5281
< 0.1%

Base_pay
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4884
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40046.18771
Minimum2035.6
Maximum79988.296
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2021-12-05T20:02:01.575204image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum2035.6
5-th percentile23662.82554
Q133744.02163
median40231.75141
Q346764.83697
95-th percentile55855.84708
Maximum79988.296
Range77952.696
Interquartile range (IQR)13020.81534

Descriptive statistics

Standard deviation10112.34245
Coefficient of variation (CV)0.2525169818
Kurtosis1.083725523
Mean40046.18771
Median Absolute Deviation (MAD)6508.79148
Skewness-0.365034841
Sum200230938.5
Variance102259469.9
MonotonicityNot monotonic
2021-12-05T20:02:01.731387image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40046.1877123
 
0.5%
53351.5190221
 
0.4%
56174.4773416
 
0.3%
61235.5123912
 
0.2%
60807.265311
 
0.2%
72278.728
 
0.2%
54310.979978
 
0.2%
57671.409396
 
0.1%
50544.824345
 
0.1%
54699.294265
 
0.1%
Other values (4874)4885
97.7%
ValueCountFrequency (%)
2035.61
< 0.1%
2279.2481
< 0.1%
2358.661
< 0.1%
2450.0481
< 0.1%
24981
< 0.1%
2577.6921
< 0.1%
2582.21
< 0.1%
2583.3428881
< 0.1%
2611.6921
< 0.1%
2672.9321
< 0.1%
ValueCountFrequency (%)
79988.2961
 
< 0.1%
78388.281
 
< 0.1%
77054.721
 
< 0.1%
74274.361
 
< 0.1%
72278.728
0.2%
70275.721
 
< 0.1%
68255.822591
 
< 0.1%
68149.018931
 
< 0.1%
67659.88281
 
< 0.1%
66052.994371
 
< 0.1%

Bonus
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct5000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4991.096428
Minimum254.45
Maximum9998.537
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2021-12-05T20:02:02.059434image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum254.45
5-th percentile2898.907326
Q14194.51695
median5028.968925
Q35845.604624
95-th percentile6950.16793
Maximum9998.537
Range9744.087
Interquartile range (IQR)1651.087674

Descriptive statistics

Standard deviation1268.848087
Coefficient of variation (CV)0.2542223148
Kurtosis0.9593172706
Mean4991.096428
Median Absolute Deviation (MAD)822.0104425
Skewness-0.3960416001
Sum24955482.14
Variance1609975.468
MonotonicityStrictly increasing
2021-12-05T20:02:02.246925image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5728.020811
 
< 0.1%
5801.837361
 
< 0.1%
4299.4868011
 
< 0.1%
4868.7206361
 
< 0.1%
4569.4083661
 
< 0.1%
4524.3979311
 
< 0.1%
4987.1841951
 
< 0.1%
5874.070951
 
< 0.1%
4600.9442551
 
< 0.1%
7313.183721
 
< 0.1%
Other values (4990)4990
99.8%
ValueCountFrequency (%)
254.451
< 0.1%
284.9061
< 0.1%
294.83251
< 0.1%
306.2561
< 0.1%
312.251
< 0.1%
322.21151
< 0.1%
322.7751
< 0.1%
322.9178611
< 0.1%
326.46151
< 0.1%
334.11651
< 0.1%
ValueCountFrequency (%)
9998.5371
< 0.1%
9798.5351
< 0.1%
9631.841
< 0.1%
9284.2951
< 0.1%
9034.841
< 0.1%
8784.4651
< 0.1%
8531.9778251
< 0.1%
8518.6273651
< 0.1%
8457.485351
< 0.1%
8448.72641
< 0.1%

Unit_Price
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3836
Distinct (%)76.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.25852201
Minimum1.44
Maximum629.511067
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2021-12-05T20:02:02.449999image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1.44
5-th percentile12.94799715
Q125.72749975
median39.205
Q358.71500025
95-th percentile124.5604981
Maximum629.511067
Range628.071067
Interquartile range (IQR)32.9875005

Descriptive statistics

Standard deviation52.24402237
Coefficient of variation (CV)1.019226078
Kurtosis53.81530851
Mean51.25852201
Median Absolute Deviation (MAD)15.455
Skewness5.989663043
Sum256292.6101
Variance2729.437874
MonotonicityNot monotonic
2021-12-05T20:02:02.590591image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
267
 
0.1%
42.2000016
 
0.1%
20.8099995
 
0.1%
20.0599995
 
0.1%
175
 
0.1%
37.2900015
 
0.1%
21.6200015
 
0.1%
48.4900025
 
0.1%
22.2199995
 
0.1%
14.385
 
0.1%
Other values (3826)4947
98.9%
ValueCountFrequency (%)
1.441
< 0.1%
1.471
< 0.1%
1.511
< 0.1%
1.521
< 0.1%
1.61
< 0.1%
1.611
< 0.1%
1.621
< 0.1%
1.631
< 0.1%
1.651
< 0.1%
1.661
< 0.1%
ValueCountFrequency (%)
629.5110671
< 0.1%
629.5100051
< 0.1%
627.8410711
< 0.1%
627.8399841
< 0.1%
625.8610781
< 0.1%
625.8600331
< 0.1%
610.0010451
< 0.1%
609.9999931
< 0.1%
604.461061
< 0.1%
604.4600061
< 0.1%

Volume
Real number (ℝ≥0)

Distinct4831
Distinct (%)96.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6761260.34
Minimum0
Maximum320868400
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2021-12-05T20:02:02.809290image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile453495
Q11283850
median2870600
Q36247100
95-th percentile22752030
Maximum320868400
Range320868400
Interquartile range (IQR)4963250

Descriptive statistics

Standard deviation16204756.36
Coefficient of variation (CV)2.396706464
Kurtosis100.1371799
Mean6761260.34
Median Absolute Deviation (MAD)1969750
Skewness8.709735196
Sum3.38063017 × 1010
Variance2.625941288 × 1014
MonotonicityNot monotonic
2021-12-05T20:02:03.043610image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7795004
 
0.1%
5480004
 
0.1%
14434003
 
0.1%
13464003
 
0.1%
33375002
 
< 0.1%
21869002
 
< 0.1%
4112002
 
< 0.1%
21050002
 
< 0.1%
59755002
 
< 0.1%
19324002
 
< 0.1%
Other values (4821)4974
99.5%
ValueCountFrequency (%)
01
< 0.1%
37001
< 0.1%
51001
< 0.1%
98001
< 0.1%
100001
< 0.1%
194001
< 0.1%
322001
< 0.1%
455001
< 0.1%
951001
< 0.1%
973001
< 0.1%
ValueCountFrequency (%)
3208684001
< 0.1%
2234868001
< 0.1%
2201047001
< 0.1%
2156202001
< 0.1%
2095213001
< 0.1%
2052579001
< 0.1%
2000706001
< 0.1%
1980786001
< 0.1%
1951171001
< 0.1%
1926099001
< 0.1%

openingbalance
Real number (ℝ≥0)

HIGH CORRELATION

Distinct2986
Distinct (%)59.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.73326344
Minimum3.68
Maximum313.9039044
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2021-12-05T20:02:03.231034image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum3.68
5-th percentile12.32789947
Q126.39763289
median33.119999
Q342.52500025
95-th percentile109.0624981
Maximum313.9039044
Range310.2239044
Interquartile range (IQR)16.12736736

Descriptive statistics

Standard deviation32.57885267
Coefficient of variation (CV)0.7998095395
Kurtosis24.51621409
Mean40.73326344
Median Absolute Deviation (MAD)7.49049875
Skewness4.094790645
Sum203666.3172
Variance1061.381641
MonotonicityNot monotonic
2021-12-05T20:02:03.387280image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.1199991479
29.6%
31.65
 
0.1%
20.9599995
 
0.1%
26.5300014
 
0.1%
43.8899994
 
0.1%
24.5400014
 
0.1%
32.2900014
 
0.1%
54.0299994
 
0.1%
38.0999984
 
0.1%
31.994
 
0.1%
Other values (2976)3483
69.7%
ValueCountFrequency (%)
3.681
< 0.1%
3.711
< 0.1%
3.751
< 0.1%
3.851
< 0.1%
4.211
< 0.1%
4.231
< 0.1%
4.261
< 0.1%
4.311
< 0.1%
4.41
< 0.1%
4.791
< 0.1%
ValueCountFrequency (%)
313.90390441
< 0.1%
313.78879181
< 0.1%
313.24325981
< 0.1%
310.7200011
< 0.1%
310.6700131
< 0.1%
309.3699951
< 0.1%
308.9599911
< 0.1%
308.8999941
< 0.1%
308.010011
< 0.1%
307.2000121
< 0.1%

closingbalance
Real number (ℝ≥0)

HIGH CORRELATION

Distinct4011
Distinct (%)80.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.57782761
Minimum3.68
Maximum313.6886942
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2021-12-05T20:02:03.652843image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum3.68
5-th percentile11.2195
Q121.99
median33.34
Q351.1175005
95-th percentile122.3109982
Maximum313.6886942
Range310.0086942
Interquartile range (IQR)29.1275005

Descriptive statistics

Standard deviation37.14851225
Coefficient of variation (CV)0.8524636102
Kurtosis15.83527458
Mean43.57782761
Median Absolute Deviation (MAD)13.85711096
Skewness3.22636805
Sum217889.1381
Variance1380.011962
MonotonicityNot monotonic
2021-12-05T20:02:03.824644image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.9199987
 
0.1%
357
 
0.1%
18.46
 
0.1%
22.276
 
0.1%
43.665
 
0.1%
33.255
 
0.1%
33.345
 
0.1%
38.935
 
0.1%
53.255
 
0.1%
255
 
0.1%
Other values (4001)4944
98.9%
ValueCountFrequency (%)
3.681
< 0.1%
3.761
< 0.1%
3.861
< 0.1%
4.211
< 0.1%
4.221
< 0.1%
4.281
< 0.1%
4.291
< 0.1%
4.311
< 0.1%
4.331
< 0.1%
4.411
< 0.1%
ValueCountFrequency (%)
313.68869421
< 0.1%
312.30731581
< 0.1%
312.20530841
< 0.1%
311.8399961
< 0.1%
311.2900091
< 0.1%
310.83045861
< 0.1%
310.6700131
< 0.1%
308.9599911
< 0.1%
308.7699891
< 0.1%
308.5799871
< 0.1%

low
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4014
Distinct (%)80.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.03412904
Minimum3.65
Maximum312.4324379
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2021-12-05T20:02:04.019362image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum3.65
5-th percentile10.9885
Q121.71874969
median32.880001
Q350.415
95-th percentile121.332002
Maximum312.4324379
Range308.7824379
Interquartile range (IQR)28.69625031

Descriptive statistics

Standard deviation36.76064128
Coefficient of variation (CV)0.8542206406
Kurtosis15.91827818
Mean43.03412904
Median Absolute Deviation (MAD)13.67
Skewness3.233666708
Sum215170.6452
Variance1351.344747
MonotonicityNot monotonic
2021-12-05T20:02:04.159956image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32.3800016
 
0.1%
24.415
 
0.1%
25.7199995
 
0.1%
355
 
0.1%
33.1699985
 
0.1%
24.255
 
0.1%
21.8600014
 
0.1%
25.764
 
0.1%
33.54
 
0.1%
45.274
 
0.1%
Other values (4004)4953
99.1%
ValueCountFrequency (%)
3.652
< 0.1%
3.721
< 0.1%
3.831
< 0.1%
4.081
< 0.1%
4.131
< 0.1%
4.151
< 0.1%
4.211
< 0.1%
4.221
< 0.1%
4.271
< 0.1%
4.661
< 0.1%
ValueCountFrequency (%)
312.43243791
< 0.1%
311.08108911
< 0.1%
310.95500081
< 0.1%
309.61002811
< 0.1%
309.4200131
< 0.1%
308.489991
< 0.1%
307.3999941
< 0.1%
305.7999881
< 0.1%
305.4599911
< 0.1%
305.4500121
< 0.1%

Unit_Sales
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1434
Distinct (%)28.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.84151
Minimum18.25
Maximum118.75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2021-12-05T20:02:04.331820image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum18.25
5-th percentile19.65
Q135.5
median70.5
Q389.95
95-th percentile107.6525
Maximum118.75
Range100.5
Interquartile range (IQR)54.45

Descriptive statistics

Standard deviation30.13967985
Coefficient of variation (CV)0.4648207583
Kurtosis-1.25800149
Mean64.84151
Median Absolute Deviation (MAD)24.1
Skewness-0.2254472442
Sum324207.55
Variance908.4003015
MonotonicityIncreasing
2021-12-05T20:02:04.566141image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19.8536
 
0.7%
20.0535
 
0.7%
19.932
 
0.6%
19.732
 
0.6%
20.2532
 
0.6%
2031
 
0.6%
19.5530
 
0.6%
20.1529
 
0.6%
19.829
 
0.6%
19.9529
 
0.6%
Other values (1424)4685
93.7%
ValueCountFrequency (%)
18.251
 
< 0.1%
18.41
 
< 0.1%
18.71
 
< 0.1%
18.751
 
< 0.1%
18.84
0.1%
18.854
0.1%
18.91
 
< 0.1%
18.955
0.1%
195
0.1%
19.057
0.1%
ValueCountFrequency (%)
118.751
< 0.1%
118.651
< 0.1%
118.62
< 0.1%
117.81
< 0.1%
117.61
< 0.1%
117.451
< 0.1%
117.21
< 0.1%
117.151
< 0.1%
116.851
< 0.1%
116.81
< 0.1%

Total_Sales
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4706
Distinct (%)94.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2269.56846
Minimum18.8
Maximum8684.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2021-12-05T20:02:04.722324image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum18.8
5-th percentile49.8475
Q1389.2125
median1395.65
Q33722.3375
95-th percentile6889.81
Maximum8684.8
Range8666
Interquartile range (IQR)3333.125

Descriptive statistics

Standard deviation2264.62695
Coefficient of variation (CV)0.9978227094
Kurtosis-0.2065550055
Mean2269.56846
Median Absolute Deviation (MAD)1215.175
Skewness0.976270175
Sum11347842.3
Variance5128535.222
MonotonicityNot monotonic
2021-12-05T20:02:04.894189image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1395.6516
 
0.3%
19.756
 
0.1%
20.156
 
0.1%
19.456
 
0.1%
20.26
 
0.1%
45.35
 
0.1%
20.455
 
0.1%
19.555
 
0.1%
20.055
 
0.1%
20.255
 
0.1%
Other values (4696)4935
98.7%
ValueCountFrequency (%)
18.81
 
< 0.1%
18.851
 
< 0.1%
18.91
 
< 0.1%
191
 
< 0.1%
19.051
 
< 0.1%
19.12
< 0.1%
19.151
 
< 0.1%
19.24
0.1%
19.251
 
< 0.1%
19.32
< 0.1%
ValueCountFrequency (%)
8684.81
< 0.1%
8672.451
< 0.1%
8564.751
< 0.1%
8529.51
< 0.1%
8496.71
< 0.1%
8477.71
< 0.1%
8477.61
< 0.1%
8476.51
< 0.1%
8468.21
< 0.1%
8456.751
< 0.1%

Months
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct73
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.1848
Minimum0
Maximum72
Zeros8
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2021-12-05T20:02:05.066023image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q18
median28
Q355
95-th percentile72
Maximum72
Range72
Interquartile range (IQR)47

Descriptive statistics

Standard deviation24.63672954
Coefficient of variation (CV)0.7654771676
Kurtosis-1.383097498
Mean32.1848
Median Absolute Deviation (MAD)22
Skewness0.2572738005
Sum160924
Variance606.9684426
MonotonicityNot monotonic
2021-12-05T20:02:05.269068image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1436
 
8.7%
72269
 
5.4%
2180
 
3.6%
3141
 
2.8%
4130
 
2.6%
71128
 
2.6%
7102
 
2.0%
597
 
1.9%
894
 
1.9%
1288
 
1.8%
Other values (63)3335
66.7%
ValueCountFrequency (%)
08
 
0.2%
1436
8.7%
2180
3.6%
3141
 
2.8%
4130
 
2.6%
597
 
1.9%
665
 
1.3%
7102
 
2.0%
894
 
1.9%
981
 
1.6%
ValueCountFrequency (%)
72269
5.4%
71128
2.6%
7075
 
1.5%
6967
 
1.3%
6871
 
1.4%
6765
 
1.3%
6666
 
1.3%
6555
 
1.1%
6458
 
1.2%
6353
 
1.1%

Education
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
PG
2979 
Graduation
1980 
Intermediate
 
27
High School or less
 
14

Length

Max length19
Median length2
Mean length5.2696
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHigh School or less
2nd rowHigh School or less
3rd rowHigh School or less
4th rowHigh School or less
5th rowHigh School or less

Common Values

ValueCountFrequency (%)
PG2979
59.6%
Graduation1980
39.6%
Intermediate27
 
0.5%
High School or less14
 
0.3%

Length

2021-12-05T20:02:05.503387image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-05T20:02:05.597150image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
pg2979
59.1%
graduation1980
39.3%
intermediate27
 
0.5%
high14
 
0.3%
school14
 
0.3%
or14
 
0.3%
less14
 
0.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2021-12-05T20:01:52.549730image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-05T20:01:27.523240image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-05T20:01:30.266680image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-05T20:01:32.512652image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-05T20:01:35.178088image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-05T20:01:37.087801image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-05T20:01:39.173818image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-05T20:01:41.479452image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-05T20:01:43.576976image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-05T20:01:45.649163image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-05T20:01:47.694735image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-05T20:01:49.872348image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-05T20:01:52.712913image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-05T20:01:28.377012image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-05T20:01:30.436371image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-05T20:01:33.213353image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-05T20:01:35.345554image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-05T20:01:37.246470image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-05T20:01:39.531991image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-05T20:01:41.694165image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-05T20:01:43.751140image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-05T20:01:45.826952image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-05T20:01:47.879777image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-05T20:01:50.029717image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-05T20:01:52.911449image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-05T20:01:28.583935image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-05T20:01:30.628703image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-05T20:01:33.417921image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-05T20:01:35.505421image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-05T20:01:37.427377image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-05T20:01:39.725773image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-05T20:01:41.881107image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-05T20:01:43.936959image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-05T20:01:46.005144image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-05T20:01:48.067115image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-05T20:01:50.192386image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-05T20:01:53.082793image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-05T20:01:28.790967image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-05T20:01:30.826175image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-05T20:01:33.595643image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-05T20:01:35.677289image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-05T20:01:37.599454image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-05T20:01:39.924358image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-05T20:01:42.090776image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-05T20:01:44.154807image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-05T20:01:46.175123image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-05T20:01:48.260418image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-05T20:01:50.356033image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-05T20:01:53.238869image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-05T20:01:28.945647image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-05T20:01:30.993072image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-05T20:01:33.768034image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-05T20:01:35.837332image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-05T20:01:37.840976image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-05T20:01:40.085401image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-05T20:01:42.246996image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-05T20:01:44.292713image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-05T20:01:46.337365image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-05T20:01:48.415532image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-05T20:01:50.498894image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-05T20:01:53.427241image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-05T20:01:29.106028image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-05T20:01:31.162004image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-05T20:01:33.940660image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-05T20:01:35.987370image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-05T20:01:37.998145image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-05T20:01:40.247446image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-05T20:01:42.409373image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-05T20:01:44.479935image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-05T20:01:46.520826image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-05T20:01:48.598482image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-05T20:01:50.691686image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-05T20:01:53.586235image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-05T20:01:29.286423image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-05T20:01:31.339908image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-05T20:01:34.111878image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-05T20:01:36.170698image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-05T20:01:38.210676image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-05T20:01:40.434233image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-05T20:01:42.581826image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-05T20:01:44.644261image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-05T20:01:46.690237image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-05T20:01:48.759097image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-05T20:01:50.843987image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-05T20:01:53.778795image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-05T20:01:29.445825image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-05T20:01:31.507246image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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Correlations

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Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-12-05T20:02:06.534427image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-12-05T20:02:06.809404image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-12-05T20:02:07.106178image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2021-12-05T20:02:07.527952image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-12-05T20:01:54.801159image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-12-05T20:01:56.000472image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

GenderBusinessDependanciesCallsTypeBillingRatingAgeSalaryBase_payBonusUnit_PriceVolumeopeningbalanceclosingbalancelowUnit_SalesTotal_SalesMonthsEducation
0FemaleNoNoYesMonth-to-monthNoYes185089.000002035.600000254.4500003.77212266003.75003.7603.6518.2518.800High School or less
1FemaleNoNoYesMonth-to-monthNoYes195698.120002279.248000284.9060003.74104628003.85003.6803.6518.4018.850High School or less
2MaleNoNoYesMonth-to-monthYesNo225896.650002358.660000294.8325003.89187610004.23004.2903.7218.7018.900High School or less
3FemaleYesNoYesMonth-to-monthYesYes216125.120002450.048000306.2560004.35661306004.26004.3103.8318.7519.000High School or less
4MaleNoNoYesMonth-to-monthYesYes236245.000002498.000000312.2500004.34268682004.79004.4104.0818.8019.051High School or less
5MaleNoNoYesTwo yearYesNo236444.230002577.692000322.2115004.37298696005.88005.0404.1318.8019.101High School or less
6MaleNoYesNoTwo yearYesNo236455.500002582.200000322.7750004.42252392006.09255.5904.1518.8019.101High School or less
7FemaleNoNoYesOne yearYesNo246458.357222583.342888322.9178614.44283075006.10005.6704.2118.8019.151Intermediate
8FemaleYesNoYesMonth-to-monthYesYes246529.230002611.692000326.4615004.45242956006.15006.1704.2718.8519.201Intermediate
9MaleNoNoYesMonth-to-monthYesNo436682.330002672.932000334.1165004.41176716006.26006.0954.2218.8519.201Intermediate

Last rows

GenderBusinessDependanciesCallsTypeBillingRatingAgeSalaryBase_payBonusUnit_PriceVolumeopeningbalanceclosingbalancelowUnit_SalesTotal_SalesMonthsEducation
4990MaleNoNoYesMonth-to-monthNoNo70168974.528061235.512398448.726400312.50000031720033.119999223.960007307.399994116.858672.4572PG
4991MaleYesNoYesTwo yearNoNo70169149.707067659.882808457.485350309.66000444350033.119999219.080002302.779999117.158684.8072PG
4992MaleYesNoYesOne yearNoNo71170372.547368149.018938518.627365312.70001229530033.119999238.089996308.489990117.201395.6572PG
4993MaleNoNoYesMonth-to-monthYesYes71170639.556568255.822598531.977825314.00000029460033.119999237.899994309.420013117.451395.6572PG
4994MaleNoNoYesMonth-to-monthYesNo71175689.300070275.720008784.465000625.861078798710033.119999238.470001302.048370117.601395.6572PG
4995FemaleNoNoYesMonth-to-monthNoNo72180696.800072278.720009034.840000629.511067392700033.119999293.838840310.955001117.801395.6572PG
4996MaleNoNoYesMonth-to-monthYesNo73185685.900074274.360009284.295000627.841071603190033.119999301.311314309.610028118.601395.6572PG
4997MaleNoNoYesMonth-to-monthYesNo74192636.800077054.720009631.840000625.860033794940033.119999306.040009303.483494118.601395.6572PG
4998MaleYesNoYesMonth-to-monthYesYes74195970.700078388.280009798.535000629.510005390840033.119999308.579987312.432438118.651395.6572PG
4999MaleNoYesYesTwo yearYesNo88199970.740079988.296009998.537000627.839984600330033.119999312.307316311.081089118.751395.6572PG